Synaptic connectome of the ctenophore apical organ

Abstract

text in bold italic underline

Introduction

Understanding how neurons evolved and what their initial functions were is key to elucidating the processes by which animals developed flexible behaviors. Neurons do not function in isolation; rather, they form networks that integrate and process information, enabling the emergence of complex behaviors. Therefore, understanding the structure and function of neural circuits is essential for investigating the mechanisms underlying animal behavior.

Recent comparative genomic studies (Li et al., 2021; Ryan et al., 2013; Whelan et al., 2017, 2015; Whelan and Halanych, 2023) and chromosome-level synteny analyses across different animal phyla (Schultz et al., 2023) suggest that ctenophores are the sister group to all other metazoans and were the first animals to evolve a nervous system. This has led to attempts to compare genes and molecular modules in ctenophores using single-cell analyses, in which neuropeptides have been employed as neuronal markers (Hayakawa et al., 2022; Sachkova et al., 2021; Sebé-Pedrós et al., 2018). However, the homologous relationships between ctenophore genes and those of other animals are highly divergent, limiting the feasibility of sequence-based comparisons. Given these challenges, we considered an alternative approach: linking neural circuits to physiological responses to gain insight into neural function.

Despite their phylogenetic position, ctenophores exhibit sophisticated and dynamic behavioral patterns in response to a wide range of external stimuli. These behaviors are thought to be regulated by multiple neural systems, including the subepithelial nerve net (SNN), mesogleal neurons, an apical organ, and tentacle nerves. However, the mechanisms by which these neural systems control behavior remain largely unexplored.

Among these, we focused on the apical organ of ctenophores. In many bilaterian and non-bilaterian animals, an apical nervous system is located at one pole of the body, forming a specialized sensory structure that governs physiological functions and motor activities. In ctenophores, ultrastructural analyses have revealed that the aboral pole contains a diverse array of sensory cells and neuronal components (Hernandez-Nicaise, 1974, 1973; HERNANDEZ-NICAISE, 1968) . Additionally, immunostaining using an anti-tyrosylated α-tubulin antibody has identified a neural structure called the deep nerve net within the apical organ, suggesting its involvement in neural regulation of sensory-motor control (Jager et al., 2010).

In this study, we specifically examined the gravity-sensing system within the apical organ. The apical organ of ctenophores contains a dense mineralized mass known as the statocyst, supported by four mechanosensory ciliated cells called balancer cells (Noda and Tamm, 2014). The ciliary movements of these balancer cells function as pacemakers for the eight rows of comb plates that control posture and swimming through mechanotransduction (Tamm, 2014) . Synaptic connections projecting to the balancer cells have also been reported (Aronova, 1974). Therefore, we conducted a connectome analysis to characterize the network structure of the apical organ and elucidate the neural circuits that link sensory input to behavioral responses in ctenophores.

Results

Volume EM reconstruction of the Mnemiopsis apical organ

First, we embedded the entire body of the Mnemiopsis leidyi cydippid-stage larvae, five days post-fertilization, in Epon resin following high-pressure freezing. To reconstruct all synaptic-resolution connections among the cells comprising the apical organ, also known as the aboral organ, we prepared approximately 1,000 ultra-thin serial sections (50 nm thick) from the aboral tip of the larval body embedded in the resin block. Using a scanning electron microscope (Zeiss Gemini 500) with a resolution of 2.0 nm/pixel, we imaged only the region containing the apical organ. Subsequently, 620 of these images were stitched and aligned using TrakEM2. The resulting volumetric EM dataset had dimensions of 60 μm × 40 μm × 30 μm. We traced and annotated all cells in this dataset, ultimately reconstructing 909 cells, each with both nuclei and cell bodies intact.

Cells containing cilia were traced down to the tips of the cilia, and basal bodies were annotated accordingly. For neuronal skeletonization, nodes were placed to interconnect the profiles of the same neuron’s processes across layers, extending the skeleton until all branches were fully traced. Each node was tagged, and skeletons were named and assigned multi-level annotations. As described later, many neurons we identified formed loop-like structures (anastomosed neurons), wherein separated branches often rejoined either the main trunk or other branches. In such cases, branch nodes were placed near the closest existing node and annotated accordingly. The entire skeletonized volume was composed of 134,591 nodes. However, 88 fragments could not be attached to somata-associated skeletons. Most of these fragments represented short skeletal branches that could not be traced beyond gaps or low-quality layers.

Next, we decided to divide the entire apical organ into four broad quadrants to facilitate grouping the identified cells. The general body plan of ctenophores, when viewed from the aboral side, exhibits biradial symmetry around the positions of the anal pores. This symmetry corresponds to the four blastomeres present at the four-cell stage during early embryonic development. We categorized the traced cells into four quadrants and designated these groups as Q1 through Q4 for clarity and consistency.

Figure 1. Morphology of the apical organ in *Mnemiopsis leidyi*
(A) Whole-body image of a 5-day-old M. leidyi cydippid larva in lateral view, observing the tentacular plane. The boxed region indicates the apical organ. Scale bar: 100 µm.
(B) Enlarged views of the apical organ from different perspectives in a 5-day-old M. leidyi cydippid larva. The left panel shows the apical organ from a aboral view. The middle panel presents a sagittal view of the apical organ. The right panel provides a tentacular plane view of the apical organ. Scale bar: 10 µm.
(C) Schematic diagram of the serial sectioning process. The entire cydippid larva was embedded in resin, trimmed around the apical organ, and sectioned into serial ultrathin sections, which were collected on glass slides in a ribbon-like arrangement.
(D) Example of cell tracing using the collaborative annotation toolkit CATMAID for large-scale electron microscopy image datasets. The spherical objects indicate nuclear positions, while the lines represent the traced cell centers. Scale bar: 10 µm.
(E) Three-dimensional reconstruction of all cells composing the apical organ, displayed from different perspectives. The left panel shows a aboral view of the apical organ. The middle panel presents a sagittal plane view. The right panel provides a tentacular plane view. Cells are color-coded according to their types. Scale bar: 10 µm.

Identification of Synaptic Structures in Syncytium Neurons

Classical neural staining techniques do not provide clear images of the neurons at the aboral pole. However, ultrastructural studies have provided morphological evidence of elements resembling neurons, based on synaptic structures, located on the epithelial floor of the apical organs (Hernandez-Nicaise, 1973; Horridge and Mackay, 1964). Based on these findings, we identified synaptic structures characteristic of ctenophore neurons in our data, using previously identified pre-synaptic triad morphological features, such as single-layered vesicles, smooth endoplasmic reticulum, and tightly packed mitochondria. In our study, we specifically identified synaptic sites and marked mitochondria (orange) as synaptic nodes. The regions where synaptic vesicles align were marked as connectors (light blue arrows) between cells across the membrane. These connectors link the synaptic nodes of the pre-synaptic cytoskeleton to the partner nodes of the post-synaptic cytoskeleton. As previously reported, the specialization of post-synaptic structures in ctenophores is not apparent, so we recognized synaptic vesicle clusters on the pre-synaptic membrane as the point of reference. Synapses were identified as either monoadic or polyadic, with one pre-synaptic neuron forming connections with one or multiple post-synaptic cells.

As mentioned above, following the identification of the presynaptic structures, we reconstructed three major Syncytial neurons. Each of these Syncytial neurons was a cell with multiple nuclei, with membranes fused by continuous plasma membranes. These neurons are distinct in morphology from the Syncytial subepithelial nerve net (SNN) neurons with blebbed morphology previously reconstructed in 3D (Burkhardt_2023?). Our findings represent the second documented discovery of Syncytial neurons in ctenophores. Furthermore, these neurons exhibited clear morphological differences when compared to other sensory cells reported in the same study, such as mesogleal neurons and sensory cells with presynaptic structures and cilia (types 1-4)(Burkhardt_2023?). Based on their distinct spatial relationships, we were able to classify these three Syncytial neurons into two categories. The first type is a larger “AO neuron_Q1234,” which possesses four (or possibly six?) nuclei spanning four quadrants. It contained X presynaptic structures. The second type consists of “AO neuron_Q12” and “AO neuron_Q34,” each having two nuclei spanning two quadrants, and each containing X presynaptic structures.

**Figure 2. Reconstruction of Three Syncytial Neurons in the Apical Organ**
(A) Representative electron micrographs of presynaptic triad structures within the dataset. The positions of mitochondria are marked in orange. Postsynaptic target cells were inferred based on the locations of synaptic vesicles and the endoplasmic reticulum, with light blue arrows indicating the direction across the cell membrane. The left panel illustrates a monadic structure, while the right panel shows a polyadic (or dyadic) structure. Scale bar: 500 nm.
(B) 3D reconstruction of the syncytial apical organ (AO) neuron Q1Q2Q3Q4, which spans all four quadrants and contains multiple nuclei (light blue). The spheres represent the positions of individual nuclei. The left panel shows a aboral view of the apical organ, the middle panel presents a sagittal plane view, and the right panel provides a tentacular plane view.
(C) 3D reconstruction of two syncytial apical organ neurons, AO neuron Q1Q2 (pink) and AO neuron Q3Q4 (orange), each spanning two quadrants and containing multiple nuclei. The spheres indicate the positions of the nuclei. The left panel shows a aboral view of the apical organ, the middle panel presents a sagittal plane view, and the right panel provides a tentacular plane view.
(D) Localization of mitochondria within AO neuron Q1Q2Q3Q4. Red indicates mitochondria associated with the presynaptic triad structures, yellow marks mitochondria containing synaptic vesicles but lacking a clearly defined presynaptic triad, blue represents mitochondria with unclear synaptic vesicles, and black denotes mitochondria where no synaptic vesicles were identified.

Identification the Gravity-Sensitive Neural Circuit via the Syncytial Neurons Network

We discovered that AO neurons form synaptic connections with each other, and that AO neuron_Q1234 forms self-synapses, or “autapses.” To our knowledge, previous reports have not identified synaptic connections between subepithelial nerve net (SNN) neurons. Thus, our results represent the first report of synaptic connectivity between neurons in the apical organ, forming a network in ctenophores (subject to confirmation). These AO neuron networks were found to form many presynaptic structures in relation to the gravity-sensing balancer cells.

Balancer cells are monociliated cells, and their cilia protrude longer than those of other monociliated cells. Moreover, the cilia of balancer cells are bundled into four groups at the center of the apical organ, forming a compound cilium. Based on these features, we identified and classified the balancer cells. The cellular arrangement differed clearly when viewed in the lateral view, sagittal plane, and tentacular plane, with the cell bodies gathering toward the apical organ in the tentacular plane. In each quadrant, the number of cells was as follows: Q1: 37, Q2: 32, Q3: 32, Q4: 28. Each cell contained 3 to 10 ? mitochondria.

From AO neuron_Q1Q2, synapses were formed with 6 of the 37 balancer cells in the Q1 region, and 8 of the 32 balancer cells in the Q2 region. Similarly, from AO neuron_Q3Q4, synapses were formed with 1 of the 32 balancer cells in the Q3 region, and 5 of the 28 balancer cells in the Q4 region. AO neuron_Q1234 formed input synapses with balancer cells in the Q1 region (7 cells), Q2 region (11 cells), Q3 region (6 cells), and Q4 region (10 cells). Some balancer cells received inputs from both AO neuron_Q1Q2 or AO neuron_Q3Q4 and AO neuron_Q1234. While previous studies have suggested the presence of afferent synapses from balancer cells to neurons (Hernandez_Nicaise_1974?), our data did not reveal any synaptic inputs from balancer cells to AO neurons. However, we identified “bridge cells” that actively formed input synapses with AO neurons, highlighting their potential role in the network.

**Figure 3. Reconstruction of Balancer Ciliated Cells in the Apical Organ**
(A) Connectivity matrix of AO neurons. Columns represent presynaptic AO neurons, rows represent postsynaptic AO neurons, and each number indicates the number of synapses. AO neurons are interconnected via synapses, and AO neuron Q1234 forms a self-synapse, autapse.
(B) Three-dimensional reconstruction of balancer ciliated cells in all four quadrant regions. Each quadrant contains approximately 30 cells. All balancer cilia are monocilia. The spheres indicate the positions of the nuclei. The left panel shows a aboral view of the apical organ, the middle panel presents a sagittal plane view, and the right panel provides a tentacular plane view.
(C) Synaptic connections from AO neurons to balancer ciliated cells. The left panel presents an aboral view, while the right panel shows a lateral view and a tentacular plane view. Balancer ciliated cells are depicted in light gray, AO neuron Q1234 in black, AO neuron Q12 in orange, and AO neuron Q34 in blue.
(D) Connectivity map of three AO neurons (Q1234 in blue, Q12 in pink, and Q34 in orange) and balancer ciliated cells (light blue). The thickness of the arrows and the numbers correspond to the number of synapses. Synaptic structures from the same neuron targeting the same balancer cell are grouped into hexagons, with the number of cells added to the label.

Identification of Neuron-like Bridge Cells Forming Afferent Synapses in the Gravity-Sensitive Neural Circuit

From our data, we identified several cell groups that form multiple afferent synapses with AO neurons. These cells, based on their morphological features, were found to be the bridge cells first described by Tamm et al. in 2002 (Tamm_and_Tamm_2002?). These cells are characterized by bundles of elongated processes filled with microtubules that arch over the epithelial layer, resembling a bridge. They originate from the base of paired balancer cells along the tentacle surface and extend across the sagittal plane toward the base of the opposite balancer cells. In regions where the mitochondria of the bridge cells are localized (approximately 30%), a presynaptic triad structure, similar to that of AO neurons, was found, containing synaptic vesicles and smooth endoplasmic reticulum.

Our three-dimensional reconstruction data revealed that the bridge cells form two distinct cell groups across the sagittal plane, between the Q1Q2 and Q3Q4 regions. In the Q1Q2 region, 14 cells were identified, while in the Q3Q4 region, 12 cells were identified, totaling 26 bridge cells. Nearly all of these bridge cells (25 out of 26) exhibited afferent synapses from AO neurons. For bridge cells located in the Q1Q2 region, synaptic inputs came primarily from AO neuron_Q1Q2 (11 cells), AO neuron_Q1234 (1 cell), or both (2 cells). For bridge cells located in the Q3Q4 region, synaptic inputs were received from AO neuron_Q3Q4 (1 cell), AO neuron_Q1234 (7 cells), or both (1 cell). In other words, bridge cells in the Q1Q2 region mainly received input from AO neuron_Q1Q2, while those in the Q3Q4 region received input primarily from AO neuron_Q1234, showing a distinct pattern of input in the two regions.

Some bridge cells also formed afferent synapses with AO neurons. For example, bridge cells in the Q1Q2 region formed synapses with AO neuron_Q1Q2 (3 cells) or both AO neuron_Q1Q2 and AO neuron_Q1234 (2 cells). Bridge cells in the Q3Q4 region formed synapses with AO neuron_Q3Q4 (1 cell) or AO neuron_Q1234 (6 cells). A notable difference was observed between the Q1Q2 and Q3Q4 regions in the proportion of synaptic inputs from bridge cells to AO neurons.

Interestingly, in both regions, bridge cells also formed synapses with adjacent bridge cells. However, no synaptic input was found from bridge cells across the sagittal plane to those in the opposite region. To analyze the grouped synaptic connectivity graph, we classified cell types within each region, collapsed cells of the same type into a single node, summed the number of synapses, and explored directed pathways to effectors (balancer cell groups). The results revealed a feedback pathway from AO neurons through the synaptic connections formed by bridge cells, thereby shedding light on the neural circuit structure involving these cells.

**Figure 4. Reconstruction of bridge cells and gravity-sensing neural circuits in the apical organ**
(A) 3D reconstruction of bridge cells spanning the Q1Q2 and Q3Q4 quadrants. The Q1Q2-side bridge cells (8 cells) are shown in blue, while the Q3Q4-side bridge cells (6 cells) are shown in xxx color. The morphology of individual bridge cells extending across opposite quadrant regions is depicted. The spheres represent the positions of individual nuclei. The left panel shows an aboral view of the apical organ, the middle panel presents a sagittal plane view, and the right panel provides a tentacular plane view.
(B) Mitochondrial localization within bridge cells and associated presynaptic triad structures. Red indicates mitochondria associated with presynaptic triad structures, yellow marks mitochondria containing synaptic vesicles but lacking a clearly defined presynaptic triad, blue represents mitochondria with unclear synaptic vesicles, and black denotes mitochondria where no synaptic vesicles were identified. The left panel shows a dorsal view of the apical organ, the middle panel presents a sagittal plane view, and the right panel provides a tentacular plane view.
(C) Synaptic connections between AO neurons and bridge cells. The positions of synapses from AO neurons to bridge cells (red) and from bridge cells to AO neurons (light blue) are indicated. The left panel shows an aboral view, while the right panel presents a tentacular plane lateral view. Bridge cells are shown in light gray, AO neurons in Q1234 are in black, AO neurons in Q12 are in orange, and AO neurons in Q34 are in blue.
(D) Connectivity matrix of the gravity-sensing neural circuit. Columns represent presynaptic cell groups, while rows represent postsynaptic cell groups. The numbers and varying shades of blue correspond to the number of synapses.
(E) Complete connectivity map of the gravity-sensing neural circuit. Cells belonging to the same group are enclosed in hexagons, and the number of cells is added to their labels. The thickness of the arrows and the numerical values indicate the number of synapses. AO neurons in Q1234 are shown in black, AO neurons in Q12 in yellow, AO neurons in Q34 in blue, balancer cell groups in gray, and bridge cell groups in pink.

Control of Balancer Ciliary Movements by Gravity-Sensitive Neural Circuits

To investigate the function of gravity-sensitive circuits, we conducted a comparative analysis of balancer ciliary movements across different regions. Previous studies (Tamm, 1980, 1982; Lowe, 1997) have established that balancer cilia function as mechanoreceptors, with their beating frequency modulated by inclination. Furthermore, it has been suggested that differences in the asymmetry of statolith morphology and the elongation direction of balancer cilia between the tentacular and sagittal planes could result in variations in the force received from the statolith (Tamm, 2014, 2015). Building on these findings, we standardized conditions for observing balancer cilia by tilting the microscope by 90 degrees and fixing samples such that the aboral-oral axis was aligned parallel to the vertical stage, with the aboral side facing upward. Using connectome analysis, we aimed to evaluate the neural circuit differences by analyzing the control patterns of left and right balancer cilia in the tentacular and sagittal planes. Specifically, we hypothesized that differences in control between Q1 (Q2) and Q3 (Q4) regions, as well as between Q1 (Q4) and Q2 (Q3) regions, would reveal distinct functions of AO neurons (e.g., AO neuron Q12/Q34 vs. AO neuron Q1234). For the sagittal plane, we recorded balancer ciliary movements in the Q1(4) and Q2(3) regions (left and right sides of the image) and, for the tentacular plane, in the Q1(3) and Q2(4) regions, using a high-speed camera (100 fps) for 2 minutes (12,000 frames). Regions of interest (ROIs) were defined in areas where brightness changes indicated ciliary beating. We measured these brightness changes to generate dynamics graphs of balancer ciliary movements for both planes.

During the 2-minute recordings, balancer cilia exhibited movements that could be fast, slow, or stop abruptly (arrest) and start moving again (re-beat). Occasionally, large contraction responses caused the entire cydippid to move out of frame, and data from these moments were excluded. We focused on two metrics for left-right comparison: (1) the timing of ciliary arrest and (2) the timing of re-beat initiation following an arrest. Due to the low baseline beating frequency of the cilia, rapid synaptic input responses were not evaluated, suggesting that ciliary beat regulation may involve non-synaptic control mechanisms. The analysis revealed that both arrest and re-beat events were synchronized between left and right balancers in both planes within a few milliseconds, suggesting that these events are governed by synaptic input from neurons. Interestingly, while the re-beat timing showed no significant differences between the sagittal and tentacular planes, arrest timing exhibited greater variability in the tentacular plane. Comparing these findings with neural circuit diagrams suggests that in the sagittal plane, the same AO neurons (Q12 or Q34) project to both balancer cell groups, transmitting signals simultaneously and enabling synchronization. In contrast, in the tentacular plane, separate AO neurons project to different regions, potentially introducing an additional synaptic step, which may account for the observed variability in arrest timing.

**Figure 5. Differences in Balancer Cilia Movement Control Due to Variations in the Gravity-Sensing Neural Circuit Pathways**
(A) Schematic diagram of differential interference contrast (DIC) microscopy setup for imaging balancer cilia movement. The microscope was tilted 90 degrees so that the stage was positioned vertically. A monochrome CMOS camera sensitive to near-infrared (NIR) light was used, synchronized with an 850 nm strobe light source. The movement of the balancer cilia was recorded using a 40× objective lens.
(B, C) Sagittal plane view (B) and tentacular plane view (C) of the apical organ under the microscope. The direction of gravity is always oriented downward in the images. The two balancer cilia being compared are indicated by arrowheads.
(D, E) Connectivity maps of the gravity-sensing neural circuit corresponding to Figure 3E. These maps emphasize the differences in neural circuits in the sagittal plane (D) and tentacular plane (E), focusing on the balancer cell groups (blue hexagons). AO neuron Q1234 is shown in green, while AO neurons Q12 and Q34 are shown in pink.
(F) Kymograph patterns comparing the arrest and re-beat of balancer cilia movement. The left and right balancer cilia movements are compared in both the sagittal plane (left) and tentacular plane (right). The direction and length of the arrows indicate time.
(G) Graph showing the time differences in the arrest and re-beat timing of the left and right balancer cilia. S and T represent the sagittal plane and tentacular plane, respectively.

Discussion

In this study, we traced all the constituent cells of the aboral organ in five-day-old larvae of Mnemiopsis leidyi and completed a comprehensive neural map. As a result, we identified a novel type of syncytial neuron with a previously unreported morphology. Furthermore, we found that three such syncytial neurons project to distinct quadrants, integrating with balancer cell groups to form a gravity-sensing neural circuit. This study represents the first complete neural circuit map reported in ctenophores. Additionally, our analysis of balancer ciliary movements, based on neural circuit data, revealed differences in ciliary arrest control arising from circuit variations.

Identification of a Previously Unreported Syncytial Neuron

We identified three syncytial neurons with multiple nuclei in the apical organ of five-day-old M. leidyi cydippid larvae. Burkhurdt et al. (2023) previously reported that in one-day-old larvae, the sub-epithelial nerve net (SNN) contains neurons that form syncytia. However, the newly discovered syncytial neurons embedded within the apical organ cell cluster did not exhibit the characteristic “pearl necklace-like structure” of the SNN, which consists of microtubules connecting neurons. Instead, their morphology was more reminiscent of fiber cells previously described in Placozoa (Mayorova et al., 2021), with cell bodies extending through intercellular spaces. These morphological characteristics suggest that the newly identified neurons represent a second distinct type of syncytial neuron in ctenophores.

Functional Diversification of the Three Syncytial Neurons

The three identified neurons may be classified into two distinct functional types based on their spatial arrangement and their influence on the differential control of balancer ciliary movement between the quadrants. In metazoans, a single neuron can directly provide synaptic input to multiple ciliated cells at a distance, synchronizing their movements. For instance, in larvae of the annelid Platynereis dumerilii, a cholinergic motor neuron (MC) innervates multiple prototroch ciliated cells, synchronizing their motion such that activation of the neuron leads to simultaneous arrest of ciliary beating (Verasztó et al., 2017). Additionally, a pair of serotonergic neurons, designated Ser-h1, extend axons that cross at the midline; the left Ser-h1_l predominantly innervates right-sided ciliated cells, while the right Ser-h1_r mainly projects to the left-side ciliated cells. Activation of Ser-h1 induces ciliary beating initiation and increases beat frequency.

In the this study, we found that three neurons are closely positioned within the confined space of the apical organ, potentially enabling a single neuron to control distant ciliary movements in a synchronized manner. Moreover, the presence of neurons projecting to the same ciliated cells in distinct patterns suggests that these neurons may serve opposing functions—one inhibiting ciliary activity while the other promotes it, or one modulating ciliary beat frequency. While our synaptic ultrastructure analysis did not allow us to distinguish excitatory from inhibitory neurons, future electrophysiological investigations will be essential to elucidate their functional properties.

**Figure 6. Title fig 6** pic of a larva, boxed region of AO, schematic, catmaid pic with slice, dimensions, quadrants, regions, (A) legend (B) legend.

Paracrine Signaling and Synaptic Transmission from Sensory to Neural Cells

Previous electron microscopy studies have suggested the presence of various sensory cells, including photoreceptor and mechanoreceptor cells, within the aboral organ of ctenophores (HORRIDGE, 1965; Vinnikov, 1974) ). By comparing our dataset with previously reported ultrastructural data, we successfully identified several sensory cells within the apical organ. However, notably, we did not find any clear presynaptic structures between these sensory cells and either neural or other cellular targets. In prior studies, immunostaining for specific neuropeptides in ctenophores has revealed positive signals in distinct cell populations within the apical organ (Hayakawa et al., 2022; Sachkova et al., 2021). These findings suggest that many sensory cells in the apical organ may primarily rely on neuropeptide-mediated paracrine signaling rather than direct synaptic connections, supporting the chemical brain hypothesis (Jekely, 2021). While paracrine transmission is generally slower than synaptic transmission, it allows for a greater diversity of information processing.

Conversely, previous research has demonstrated that mechanoreceptive sensory cells equipped with cilia (referred to as type 3 and type 4 cells) in ctenophores form direct synaptic connections with the sub-epithelial nerve net (SNN) and mesogleal neurons (Burkhardt et al., 2023). Based on their ultrastructural features, these sensory cells are believed to respond to physical stimuli such as water flow, vibrations, and direct contact, transmitting information rapidly to cilia and muscle cells (Hertwig, 1880; Chun, 1880; Eimer, 1880).

Surprisingly, our study identified bridge cells as the primary cell type forming synaptic inputs onto neurons in the apical organ. Bridge cells were first described by Tamm et al. based on their morphological characteristics, with cellular projections extending to the basal region of balancer cilia (Tamm and Tamm, 2002). Our findings suggest that bridge cells are electrically coupled to balancer cilia and provide rapid feedback to neurons via synaptic transmission. Future studies analyzing differential responses to various sensory stimuli will not only elucidate the functional roles of bridge cells but also provide insights into the evolutionary relationship between transmission speed and synapse development.

Circuit Variability as a Driver of Behavioral Diversity

Our study suggests that differences in neural circuits contribute to variations in the timing of ciliary arrest, which may play a role in modulating swimming direction in ctenophores (Satterlie, 2015). This finding provides evidence that increased circuit complexity contributes to behavioral diversification. By integrating multiple neurons and altering circuit structure, information can be branched, allowing for diverse motor patterns within the framework of fast synaptic transmission.

The neural cells identified in the apical organ exhibit a divergent feedforward connectivity pattern, where a single neuron forms presynaptic connections with multiple downstream targets. Such a circuit design minimizes unwanted signal interference, reduces processing delays, and enables relatively fast response execution (Luo, 2021). Moreover, the presence of multiple divergent feedforward circuits suggests that slight variations in the synchronization and phase delay of ciliary beating (within the range of a few hundred milliseconds) may have evolved as a mechanism for generating behavioral diversity.

From a broader neuroanatomical perspective, nervous system structures across various animals have been shaped through selective optimization to adapt to environmental constraints (Bullmore & Sporns, 2012; Perin et al., 2011). For example, in nematodes, the spatial arrangement of ganglia is optimized to minimize the total length of neural wiring (White et al., 1986). Similarly, in ctenophores, understanding how transmission via syncytial neurons and synaptic connections has been selectively optimized at the circuit level will provide crucial insights into the evolutionary scaling of neural networks from early nervous systems to complex brains (Farnworth and Montgomery, 2024). In particular, this research may help elucidate how circuit modularization and layered structures emerged over evolutionary time.

Materials and Methods

Data and Code Availability

The following EM image stacks, including all traces and annotations, are available at https://catmaid.jekelylab.ex.ac.uk. These datasets can be queried either within CATMAID or through the CATMAID application programming interface (API) using the catmaid or natverse packages (Bates et al., 2020). The dataset encompasses all EM images (in JPG format), skeletons, meshes, node tags, connectors, and annotations. Additionally, we provide all R scripts used for data acquisition and figure generation (Jokura et al., 2025). All plots, figures (including anatomical renderings), and figure layouts can be fully reproduced using the provided R scripts. While the scripts are mostly organized by figure, some general-purpose scripts—for tasks such as loading libraries, accessing CATMAID data, and defining common functions—are shared across multiple figures.

Specimen Preparation, Transmission Electron Microscopy, and Image Processing

Larvae of Mnemiopsis leidyi (5 days old) were cryofixed using a high-pressure freezing apparatus (BAL-TEC HPM 010, Balzers) and immediately transferred to liquid nitrogen for storage. The frozen samples were processed in a substitution medium containing 2% (w/v) osmium tetroxide and 0.5% uranyl acetate in acetone, using a cryo-substitution device (EM AFS-2, Leica). Cryo-substitution was performed by gradually raising the temperature, and the samples were embedded in epoxy resin. Serial sections of 50 nm thickness were prepared using a Reichert Jung Ultracut E ultramicrotome and a 45º DiATOME diamond knife. To enhance section adhesion and improve hydrophilicity, a conductive indium tin oxide-coated glass slide (ITO Glass, UQG Optics) was treated with air glow discharge using the PELCO easiGlow system (Ted Pella, Inc.), rendering the carbon film surface negatively charged. Section ribbons were collected on the prepared glass slides, slowly dried to ensure proper stretching, and firmly adhered to the glass surface. The sections on glass were stained with UranyLess and lead citrate (Reynolds) using airless staining bottles (Delta Microscopies). The glass slides were mounted on STEM-specific stages (Zeiss) using Copper Foil EMI Shielding Tape (3M). Imaging was performed using a Gemini SEM 500 (Zeiss) equipped with SmartSEM and Atlas 5 imaging software (Zeiss). The full dataset consisted of 620 serial sections, each 50 nm thick. The imaging resolution was 2.8 nm/pixel, using electron holography transmission at an acceleration voltage of 1.5 kV (in-lens detector, dwell time: 3 µs).

Image-Stack Realignment and Spatial Data Transformation in CATMAID

To process the image stack, we utilized the TrakEM2 plugin of FIJI (ImageJ) (version 2.0.0-rc-15/1.49k / Java 1.6.0_24 (64-bit) – 2014). A project was created, and all TIFF images were imported using the “import sequence as grid” function. Subsequently, the following filters were applied sequentially: Invert, Equalize Histogram, and Gaussian Blur.

The alignment process consisted of three stages, each progressively refining the spatial accuracy:

Rigid Alignment Initially, a rigid alignment was performed with the following parameters: least squares mode (linear feature correspondences), encompassing the entire layer range with the first layer as the reference. Only visible images were used, without propagation. The alignment was executed with an initial Gaussian blur of 1.6 pixels, three steps per scale octave, a minimum image size of 512 pixels, and a maximum of 2048 pixels. Additional parameters included a feature descriptor size of 8, orientation bins of 8, and a closest ratio of 0.92. The alignment allowed clearing the cache, using 32 feature extraction threads, a maximal alignment error of 100 pixels, a minimal inlier ratio of 0.20, and a minimum of 12 inliers. The expected and desired transformations were set to rigid, with testing multiple hypotheses (tolerance: 5.00 pixels) and considering up to 5 neighboring layers, giving up after 5 failures. Regularization was performed with a maximal iteration of 1000, a maximal plateau width of 200, and a rigid lambda of 0.10.

Affine Alignment Following the rigid alignment, an affine alignment was applied using similar parameters, except the expected and desired transformations were set to affine. The minimal image size was reduced to 64 pixels, while the other parameters (e.g., Gaussian blur, feature descriptor size, inliers, and testing hypotheses) remained unchanged to ensure consistent processing.

Elastic Alignment Two iterations of elastic alignment were performed to fine-tune the spatial data. Key parameters included a block matching layer scale of 0.05, a search radius of 200 pixels, a block radius of 2000 pixels, and a resolution of 60. Correlation filters were employed with a minimal PMCC r of 0.10, a maximal curvature ratio of 1000, and a maximal second-best r/best r of 0.90. A local smoothness filter was applied with the approximate local transformation set to affine, a local region sigma of 1000 pixels, and an absolute maximal local displacement of 10 pixels (relative maximal displacement: 3.00). Pre-aligned layers were tested for up to 4 neighboring layers. The elastic alignment used a rigid approximation, maximal iterations of 3000, a plateau width of 200, spring mesh stiffness of 0.01, and a maximal stretch of 2000 pixels. A legacy optimizer was employed to enhance performance.

After each alignment stage, the project was saved as an XML file under a unique name to preserve iterative progress. Finally, the images were exported from FIJI using TrakEM2 in a format compatible with CATMAID.

Neuron Tracing, Synapse Annotation, and Review

The reconstruction of cells and connectivity was performed using CATMAID (Saalfeld et al., 2009), analyzing a total of 620 serial sections. To digitally reconstruct all neurons within the serial TEM dataset, we utilized the collaborative web-based application CATMAID, installed on a local server (Saalfeld et al., 2009; Schneider-Mizell et al., 2016). To mark the locations of cell bodies, we placed tags at the approximate center of each nucleus within the dataset. At each nuclear center, the radius of the single node was adjusted according to the size of the cell body in that specific layer. All skeletons were rooted at their respective cell bodies, and nodes were tagged as “soma.” Synapses were identified based on four key structural features: the cell membrane, synaptic vesicles, the endoplasmic reticulum, and mitochondria. Most synapses could be verified across consecutive sections, ensuring accurate annotation and connectivity mapping.

Cell Nomenclature and Annotations

Each cell is assigned a specific type name based on its category (balancer, biC, bridge, bristle, dense vesicle cell, dome, EF, IcMC, LB, lithocyte, monoC, non-C, plumose cell, SNN, ssCG, SSN, stCG), resulting in a total of 17 cell types. Additionally, a quadrant number is appended to the cell type name following an underscore (“_”) to indicate the cell body’s location. For example, cells located in the first quadrant are annotated with “Q1.” If a cell is situated between the first and second quadrants, the annotation “Q1Q2” is applied.

Cells of the same type within the same region are further distinguished by serial numbering. Each cell is also assigned multiple annotations, which can be utilized to query the database via CATMAID or the CATMAID API (e.g., using the R catmaid package). These annotations provide a structured and precise framework for identifying and analyzing specific cells, facilitating robust data integration and retrieval within the dataset.

This system ensures consistent and accurate tracking of cellular data, enabling comprehensive analysis of cellular structures and functions across regions.

Analysis of Balancer Cilia

We conducted the analysis using the cydippid stage of Mnemiopsis leidyi at five days post-fertilization. A coverslip with a thin layer of Vaseline applied to two edges was lightly placed on a slide glass. Filtered natural seawater was introduced under the coverslip, along with the cydippids. By gently moving the coverslip, the desired orientation for observation was adjusted, and once positioned, slight pressure was applied to immobilize the cydippids. To stabilize the statolith’s position, the microscope was tilted 90 degrees, arranging the stage vertically. The movement of the balancer cilia was observed using a differential interference contrast (DIC) microscope (Zeiss Axio Imager.M2) equipped with a highly sensitive CMOS monochrome camera optimized for the near-infrared (NIR) range (UI-3360CP-NIR-GL Rev.2, iDS). Observations were made using a 40x objective lens (Objective LD LCI Plan-Apochromat 40x/1.2 Imm Corr DIC M27) with glycerine immersion. For recording, we employed a custom-made NIR LED strobe illumination system (wavelength: 850 nm) synchronized with the camera’s exposure signals. The camera and LED strobe system were operated using the Video Capture Software BohNavi. Images were recorded at a resolution of 640×480 pixels with a frame rate of 100 fps for 2 minutes, using 0.05 ms pulses from the LED. To analyze the ciliary beating of the balancer cilia, we utilized the Multi Kymograph project in Fiji software.

Figure 6 - cilia? renderings of basal bodies, centrioles, length, axoneme structure (table), table about cell types, number of cilia etc.

Synapses

“synaptic regions with the characteristic presynaptic triad mor- phology of ctenophore nerves (i.e., a single layer of vesicles, smooth ER sac, and closely apposed mito- chondria (Hernandez-Nicaise, 1973; Horridge and Mackay, 1964)”

Figure 7 - synapses, reconstruction, annotation, numbers, distribution of partners, example EM (figure supplement - lots of EM), mitochondria (Fig suppl - mitochondrion stats - Sanja)

Gap juntions? Innexins are expressed in the apical organ in specific patches of cells in each quadrant (Ortiz et al., 2023)

Ctenophore synapses (Hernandez-Nicaise, 1973)

We found no synapses between bridge and balancer, in agreement with Tamm (Tamm and Tamm, 2002).

Figure 8 - connectome cell-type based, quadrant based, nerve net syntycia, bridge, quadrants (Fig suppl - cell-based network)

Tamm&Tamm observed synapses from neurites to bridge cells (2002). and in Mnemiopsis larvae observed synapses from bridge cells onto other cells

Inserting Figures

You can add your figures into the rendered document. We saved the figures into /manuscript/figures or /manuscript/figure_supplements and can insert them from there. We use knitr::include_graphics for this. The title and legend can also be edited, as will as the width of the output figure.

Connectivity

Physiology?

Discussion

Equations

Equations can also be inserted, Insert -> Display Math:

\[ \bar{X} = \frac{\sum_{i=1}^{n} x_{i}}{n} \]

Materials and Methods

Volume electron microscopy

Imaging of balancer activity

Cell-type annotation and connectome analysis

Data and code availability

Acknowledgements

This work was funded by … JSPS… This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 101020792).

References

Aronova M. 1974. Electron microscopic observation of the aboral organ of ctenophora. I. The gravity receptor. Zeitschrift fur mikroskopisch-anatomische Forschung 88:401–412.

Bates AS, Manton JD, Jagannathan SR, Costa M, Schlegel P, Rohlfing T, Jefferis GS. 2020. The natverse, a versatile toolbox for combining and analysing neuroanatomical data. eLife 9. doi:10.7554/elife.53350

Burkhardt P, Colgren J, Medhus A, Digel L, Naumann B, Soto-Angel JJ, Nordmann E-L, Sachkova MY, Kittelmann M. 2023. Syncytial nerve net in a ctenophore adds insights on the evolution of nervous systems. Science 380:293–297. doi:10.1126/science.ade5645

Farnworth MS, Montgomery SH. 2024. Evolution of neural circuitry and cognition. Biology Letters 20. doi:10.1098/rsbl.2023.0576

Hayakawa E, Guzman C, Horiguchi O, Kawano C, Shiraishi A, Mohri K, Lin M-F, Nakamura R, Nakamura R, Kawai E, Komoto S, Jokura K, Shiba K, Shigenobu S, Satake H, Inaba K, Watanabe H. 2022. Mass spectrometry of short peptides reveals common features of metazoan peptidergic neurons. Nature Ecology & Evolution 6:1438–1448. doi:10.1038/s41559-022-01835-7

Hernandez-Nicaise M-L. 1974. Ultrastructural evidence for a sensory-motor neuron in Ctenophora. Tissue and Cell 6:43–47. doi:10.1016/0040-8166(74)90021-4

Hernandez-Nicaise M-L. 1973. The nervous system of ctenophores III. Ultrastructure of synapses. Journal of Neurocytology 2:249–263. doi:10.1007/bf01104029

HERNANDEZ-NICAISE M-L. 1968. Specialized Connexions between Nerve Cells and Mesenchymal Cells in Ctenophores. Nature 217:1075–1076. doi:10.1038/2171075a0

HORRIDGE GA. 1965. RELATIONS BETWEEN NERVES AND CILIA IN CTENOPHORES. American Zoologist 5:357–375. doi:10.1093/icb/5.3.357

Horridge GA, Mackay B. 1964. Neurociliary synapses in pleurobrachia (ctenophora). Journal of Cell Science S3-105:163–174. doi:10.1242/jcs.s3-105.70.163

Jager M, Chiori R, Alié A, Dayraud C, Quéinnec E, Manuel M. 2010. New insights on ctenophore neural anatomy: Immunofluorescence study in Pleurobrachia pileus (Müller, 1776). Journal of Experimental Zoology Part B: Molecular and Developmental Evolution 316B:171–187. doi:10.1002/jez.b.21386

Li Y, Shen X-X, Evans B, Dunn CW, Rokas A. 2021. Rooting the Animal Tree of Life. Molecular Biology and Evolution 38:4322–4333. doi:10.1093/molbev/msab170

Noda N, Tamm SL. 2014. Lithocytes are transported along the ciliary surface to build the statolith of ctenophores. Current Biology 24:R951–R952. doi:10.1016/j.cub.2014.08.045

Ortiz J, Bobkov YV, DeBiasse MB, Mitchell DG, Edgar A, Martindale MQ, Moss AG, Babonis LS, Ryan JF. 2023. Independent innexin radiation shaped signaling in ctenophores. Molecular Biology and Evolution 40. doi:10.1093/molbev/msad025

Ryan JF, Pang K, Schnitzler CE, Nguyen A-D, Moreland RT, Simmons DK, Koch BJ, Francis WR, Havlak P, Smith SA, Putnam NH, Haddock SHD, Dunn CW, Wolfsberg TG, Mullikin JC, Martindale MQ, Baxevanis AD. 2013. The Genome of the Ctenophore Mnemiopsis leidyi and Its Implications for Cell Type Evolution. Science 342. doi:10.1126/science.1242592

Saalfeld S, Cardona A, Hartenstein V, Tomančák P. 2009. CATMAID: collaborative annotation toolkit for massive amounts of image data. Bioinformatics 25:1984–1986. doi:10.1093/bioinformatics/btp266

Sachkova MY, Nordmann E-L, Soto-Àngel JJ, Meeda Y, Górski B, Naumann B, Dondorp D, Chatzigeorgiou M, Kittelmann M, Burkhardt P. 2021. Neuropeptide repertoire and 3D anatomy of the ctenophore nervous system. Current Biology 31:5274–5285.e6. doi:10.1016/j.cub.2021.09.005

Satterlie RA. 2015. The search for ancestral nervous systems: an integrative and comparative approach. Journal of Experimental Biology 218:612–617. doi:10.1242/jeb.110387

Schneider-Mizell CM, Gerhard S, Longair M, Kazimiers T, Li F, Zwart MF, Champion A, Midgley FM, Fetter RD, Saalfeld S, Cardona A. 2016. Quantitative neuroanatomy for connectomics in Drosophila. eLife 5. doi:10.7554/elife.12059

Schultz DT, Haddock SHD, Bredeson JV, Green RE, Simakov O, Rokhsar DS. 2023. Ancient gene linkages support ctenophores as sister to other animals. Nature 618:110–117. doi:10.1038/s41586-023-05936-6

Sebé-Pedrós A, Chomsky E, Pang K, Lara-Astiaso D, Gaiti F, Mukamel Z, Amit I, Hejnol A, Degnan BM, Tanay A. 2018. Early metazoan cell type diversity and the evolution of multicellular gene regulation. Nature Ecology & Evolution 2:1176–1188. doi:10.1038/s41559-018-0575-6

Tamm SL. 2014. Formation of the statolith in the ctenophore mnemiopsis leidyi. The Biological Bulletin 227:7–18. doi:10.1086/bblv227n1p7

Tamm SL, Tamm S. 2002. Novel bridge of axon‐like processes of epithelial cells in the aboral sense organ of ctenophores. Journal of Morphology 254:99–120. doi:10.1002/jmor.10019

Vinnikov YaA. 1974. Gravity reception. Springer Berlin Heidelberg. pp. 272–321. doi:10.1007/978-3-642-80822-7_9

Whelan NV, Halanych KM. 2023. Available data do not rule out Ctenophora as the sister group to all other Metazoa. Nature Communications 14. doi:10.1038/s41467-023-36151-6

Whelan NV, Kocot KM, Moroz LL, Halanych KM. 2015. Error, signal, and the placement of Ctenophora sister to all other animals. Proceedings of the National Academy of Sciences 112:5773–5778. doi:10.1073/pnas.1503453112

Whelan NV, Kocot KM, Moroz TP, Mukherjee K, Williams P, Paulay G, Moroz LL, Halanych KM. 2017. Ctenophore relationships and their placement as the sister group to all other animals. Nature Ecology & Evolution 1:1737–1746. doi:10.1038/s41559-017-0331-3

Other Formats

Synaptic connectome of the ctenophore apical organ

Abstract

Introduction

Results

Volume EM reconstruction of the Mnemiopsis apical organ

Identification of Synaptic Structures in Syncytium Neurons

Identification the Gravity-Sensitive Neural Circuit via the Syncytial Neurons Network

Identification of Neuron-like Bridge Cells Forming Afferent Synapses in the Gravity-Sensitive Neural Circuit

Control of Balancer Ciliary Movements by Gravity-Sensitive Neural Circuits

Discussion

Identification of a Previously Unreported Syncytial Neuron

Functional Diversification of the Three Syncytial Neurons

Paracrine Signaling and Synaptic Transmission from Sensory to Neural Cells

Circuit Variability as a Driver of Behavioral Diversity

Materials and Methods

Data and Code Availability

Specimen Preparation, Transmission Electron Microscopy, and Image Processing

Image-Stack Realignment and Spatial Data Transformation in CATMAID

Neuron Tracing, Synapse Annotation, and Review

Cell Nomenclature and Annotations

Analysis of Balancer Cilia

Synapses

Connectivity

Physiology?

Discussion

Equations

Materials and Methods

Volume electron microscopy

Imaging of balancer activity

Cell-type annotation and connectome analysis

Data and code availability

Acknowledgements

References

Supplementary material